Ricerc@Sapienza

Precision oncology uses genomic evidence to match patients with treatment but often fails to
identify all patients who may respond. The transcriptome of these “hidden responders” may reveal
responsive molecular states. We describe and evaluate a machine-learning approach to classify
aberrant pathway activity in tumors, which may aid in hidden responder identification. The
algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across
The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in

Metabolic reprogramming provides critical information for clinical oncology. Using molecular
data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33
cancer types based on mRNA expression patterns of seven major metabolic processes and assessed
their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical
outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism